The goal of the Molecular and Expression Analysis (MEA) Core is to provide BCM IDDRC investigators with access to high-throughput methods that can identify and quantify global phenotypic differences between fluids, cells or tissues at the level of gene expression, protein expression, post-translational modification, and cell metabolism. Targeted versions of these molecular technologies are valuable for testing and verifying molecular outcomes in genetic models for disease, but the unbiased nature of many platforms makes them exciting tools for identifying the mechanistic basis for disease and for direct discovery of biomarkers. The RNA Profiling sub-core will enable IDDRC investigators to characterize transcriptomes at the single cell level using several complementary RNA-seq commercial platforms, and to visualize and validate by imaging gene expression patterns in tissues by RNA in situ hybridization (ISH) and imaging. Differential gene expression inferred from untargeted transcriptomics can help researchers confirm models, but it can also reveal unanticipated findings about gene regulatory networks; targeted RNA ISH can validate and visualize these findings in brain tissue. The Protein and Metabolite Profiling sub-core will provide services and expertise to identify and profile proteins, protein complexes, post-translational modifications, and metabolites. Proteomics can provide key insights into the states of protein regulatory networks that control cellular phenotypes, and differences in small molecule levels revealed by untargeted metabolomics of fluids from animal models or patients can identify biochemical imbalances and biomarkers for disease. Because processing and interpreting data generated by these platforms is challenging, the Data Analysis and Integration sub-core will provide computational and data science expertise to assist IDDRC investigators with analysis of RNA sequencing data, proteomics LC- MS data, metabolomics LC-HRMS data, and metabolomics NMR data. The Core will also develop new computational methods to extract information from data derived from the same biological samples but across different -omics platforms. By providing access to a suite of platforms for the molecular characterization of phenotype, and the data analysis expertise needed to make sense of these complex systems, the MEA Core will enable researchers to identify molecular changes that lead to or report on pathogenic mechanism in IDDs. Tracking differences between healthy and disease states across these different modalities may yield connections between the genetic, gene regulatory, and biochemical basis for neural dysfunction.